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<li><a class="reference internal" href="#">Voice Lab</a></li>
<li><a class="reference internal" href="#load-voices-tab">Load Voices Tab</a><ul>
<li><a class="reference internal" href="#load-sound-file">Load Sound File</a></li>
<li><a class="reference internal" href="#remove-sound-file">Remove Sound File</a></li>
<li><a class="reference internal" href="#start">Start</a></li>
</ul>
</li>
<li><a class="reference internal" href="#settings-tab">Settings Tab</a><ul>
<li><a class="reference internal" href="#save-results">Save Results</a><ul>
<li><a class="reference internal" href="#results-xlsx">results.xlsx</a></li>
<li><a class="reference internal" href="#settings-xlsx">settings.xlsx</a></li>
</ul>
</li>
<li><a class="reference internal" href="#measure-duration">Measure Duration</a></li>
<li><a class="reference internal" href="#measure-pitch">Measure Pitch</a></li>
<li><a class="reference internal" href="#measure-pitch-crepe">Measure Pitch Crepe</a><ul>
<li><a class="reference internal" href="#automated-pitch-floor-and-ceiling-parameters">Automated pitch floor and ceiling parameters</a></li>
</ul>
</li>
<li><a class="reference internal" href="#measure-harmonicity">Measure Harmonicity</a></li>
<li><a class="reference internal" href="#signal-to-noise-ratio">Signal to Noise Ratio</a></li>
<li><a class="reference internal" href="#measure-jitter">Measure Jitter</a></li>
<li><a class="reference internal" href="#measure-shimmer">Measure Shimmer</a></li>
<li><a class="reference internal" href="#measure-cepstral-peak-prominance-cpp">Measure Cepstral Peak Prominance (CPP)</a></li>
<li><a class="reference internal" href="#measure-formants">Measure Formants</a></li>
<li><a class="reference internal" href="#vocal-tract-estimates">Vocal Tract Estimates</a><ul>
<li><a class="reference internal" href="#average-formant">Average Formant</a></li>
<li><a class="reference internal" href="#principle-components-analysis">Principle Components Analysis</a></li>
<li><a class="reference internal" href="#formant-position">Formant Position</a></li>
<li><a class="reference internal" href="#geometric-mean">Geometric Mean</a></li>
<li><a class="reference internal" href="#formant-dispersion">Formant Dispersion</a></li>
<li><a class="reference internal" href="#vtl">VTL</a></li>
<li><a class="reference internal" href="#vtl-f">VTL Δf</a></li>
</ul>
</li>
<li><a class="reference internal" href="#measure-intensity">Measure Intensity</a></li>
<li><a class="reference internal" href="#measure-rms-energy">Measure RMS Energy</a></li>
<li><a class="reference internal" href="#measure-speech-rate">Measure Speech Rate</a></li>
<li><a class="reference internal" href="#measure-spectral-tilt">Measure Spectral Tilt</a></li>
<li><a class="reference internal" href="#measure-ltas">Measure LTAS:</a></li>
<li><a class="reference internal" href="#measure-spectral-shape">Measure Spectral Shape</a></li>
<li><a class="reference internal" href="#teva">TEVA</a></li>
</ul>
</li>
<li><a class="reference internal" href="#voice-manipulations">Voice Manipulations</a><ul>
<li><a class="reference internal" href="#manipulate-pitch">Manipulate Pitch</a></li>
<li><a class="reference internal" href="#manipulate-formants">Manipulate Formants</a></li>
<li><a class="reference internal" href="#manipulate-pitch-and-formants">Manipulate Pitch and Formants</a></li>
<li><a class="reference internal" href="#resample-sounds">Resample Sounds</a></li>
<li><a class="reference internal" href="#reverse-sounds">Reverse Sounds</a></li>
<li><a class="reference internal" href="#scale-intensity">Scale Intensity</a></li>
</ul>
</li>
<li><a class="reference internal" href="#spectrograms">Spectrograms</a></li>
<li><a class="reference internal" href="#results-tab">Results Tab</a></li>
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  <div class="section" id="voice-lab">
<h1>Voice Lab<a class="headerlink" href="#voice-lab" title="Permalink to this headline">¶</a></h1>
<div class="toctree-wrapper compound">
</div>
<p>Voice Lab is an automated voice analysis software. What this software does is allow you to measure, manipulate, and visualize many voices at once, without messing with analysis parameters. You can also save all of your data, analysis parameters, manipulated voices, and full colour spectrograms with the press of one button.</p>
<p>Voice Lab is written in Python and relies heavily on a package called parselmouth-praat. parselmouth-praat is a Python package that essentially turns Praat’s source code written in C and C++ into a Pythonic interface. What that means is that any praat measurement in this software is using actual Praat source code, so you can trust the underlying algorithms. Voice Lab figures out all of the analysis parameters for you, but you can always use your own, and these are the same parameters as in Praat, and they do the exact same thing because it is Praat’s source code powering everything. That means if you are a beginner an expert, or anything in-between, you can use this software to automate your acoustical analyses.</p>
<p>All of the code is open source and available on our GitHub repository, so if this manual isn’t in-depth enough, and you want to see exactly what’s going on, go for it. It is under the MIT license, so you are free to do what you like with the software as long as you give us credit. For more info on that license, see here.</p>
</div>
<div class="section" id="load-voices-tab">
<h1>Load Voices Tab<a class="headerlink" href="#load-voices-tab" title="Permalink to this headline">¶</a></h1>
<div class="section" id="load-sound-file">
<h2>Load Sound File<a class="headerlink" href="#load-sound-file" title="Permalink to this headline">¶</a></h2>
<p>Press this button to load sound files. You can load as many files as you like.
At the moment, Voice Lab processes the following file types:</p>
<ul class="simple">
<li><p>wav</p></li>
<li><p>mp3</p></li>
<li><p>aiff</p></li>
<li><p>ogg</p></li>
<li><p>aifc</p></li>
<li><p>au</p></li>
<li><p>nist</p></li>
<li><p>flac</p></li>
</ul>
</div>
<div class="section" id="remove-sound-file">
<h2>Remove Sound File<a class="headerlink" href="#remove-sound-file" title="Permalink to this headline">¶</a></h2>
<p>Use this button to remove the selected sound file(s) from the list.</p>
</div>
<div class="section" id="start">
<h2>Start<a class="headerlink" href="#start" title="Permalink to this headline">¶</a></h2>
<p>Pressing this begins analysis. If you want to run the default analysis, press this button.
If you want to select different analyses or adjust analysis parameters, go to the ‘Settings’ tab and press the ‘Advanced Settings’ button.
Only the files selected (in blue) will be analyzed. By default we will select all files.</p>
</div>
</div>
<div class="section" id="settings-tab">
<span id="settings"></span><h1>Settings Tab<a class="headerlink" href="#settings-tab" title="Permalink to this headline">¶</a></h1>
<p>To choose different analyses, select the <code class="code python docutils literal notranslate"><span class="name"><span class="pre">Use</span></span> <span class="name"><span class="pre">Advanced</span></span> <span class="name"><span class="pre">Settings</span></span></code> checkbox. From here, you’ll be given the option to select different analyses. You can also change any analysis parameters. If you do change analysis parameters, make sure you know what you are doing, and remember that those same analysis parameters will be used on all voice files that are selected. If you don’t alter these parameters, we determine analysis parameters automatically for you, so they are tailored for each voice to give the best measurements.</p>
<div class="section" id="save-results">
<h2>Save Results<a class="headerlink" href="#save-results" title="Permalink to this headline">¶</a></h2>
<p>Save Results saves two xlsx files. One is the results.xlsx file and one is the settings.xlsx file. Here you can choose the directory you want to save the files into. You don’t have to click on a file, just go to the directory and press the button.</p>
<div class="section" id="results-xlsx">
<h3>results.xlsx<a class="headerlink" href="#results-xlsx" title="Permalink to this headline">¶</a></h3>
<p>The results file saves all of the voice measurements that you made. Each measurement gets a separate tab in the xlsx file.</p>
</div>
<div class="section" id="settings-xlsx">
<h3>settings.xlsx<a class="headerlink" href="#settings-xlsx" title="Permalink to this headline">¶</a></h3>
<p>This file saves all of the parameters used in each measurement. Each measurement gets a separate tab in the xlsx file. This is great if you want to know what happened. It can also accompany a manuscript or paper to help others replicate analyses.</p>
</div>
</div>
<div class="section" id="measure-duration">
<span id="duration"></span><h2>Measure Duration<a class="headerlink" href="#measure-duration" title="Permalink to this headline">¶</a></h2>
<p>This measures the full duration of the sound file. There are no parameters to adjust.</p>
</div>
<div class="section" id="measure-pitch">
<span id="pitch"></span><h2>Measure Pitch<a class="headerlink" href="#measure-pitch" title="Permalink to this headline">¶</a></h2>
<p>This measures voice pitch or fundamental frequency. This uses Praat’s <code class="code python docutils literal notranslate"><span class="name"><span class="pre">Sound</span></span><span class="punctuation"><span class="pre">:</span></span> <span class="name"><span class="pre">To</span></span> <span class="name"><span class="pre">Pitch</span></span> <span class="punctuation"><span class="pre">(</span></span><span class="name"><span class="pre">ac</span></span><span class="punctuation"><span class="pre">)</span></span><span class="operator"><span class="pre">…</span></span></code>, by default. You can also use the cross-correlation algorithm: <code class="code python docutils literal notranslate"><span class="name"><span class="pre">Sound</span></span><span class="punctuation"><span class="pre">:</span></span> <span class="name"><span class="pre">To</span></span> <span class="name"><span class="pre">Pitch</span></span> <span class="punctuation"><span class="pre">(</span></span><span class="name"><span class="pre">cc</span></span><span class="punctuation"><span class="pre">)</span></span><span class="operator"><span class="pre">…</span></span></code>. For full details on these algorithms, see the <a class="reference external" href="http://www.fon.hum.uva.nl/praat/manual/Pitch.html">praat manual pitch page</a>.
<code class="code python docutils literal notranslate"><span class="name"><span class="pre">Measure</span></span> <span class="name"><span class="pre">Pitch</span></span></code> returns the following measurements:
- Minimum Pitch
- Maximum Pitch
- Mean Pitch
- Standard Deviation of Pitch
- Pitch Floor
- Pitch Ceiling</p>
<p>We use the automated pitch floor and ceiling parameters described <a class="reference internal" href="#floor-ceiling"><span class="std std-ref">here.</span></a></p>
</div>
<div class="section" id="measure-pitch-crepe">
<h2>Measure Pitch Crepe<a class="headerlink" href="#measure-pitch-crepe" title="Permalink to this headline">¶</a></h2>
<p>This is not a Praat algorithm. This measures pitch using a convolutional neural network called the Crepe algorithm:
Kim, J. W., Salamon, J., Li, P., &amp; Bello, J. P. (2018, April). Crepe: A convolutional representation for pitch estimation. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 161-165). IEEE.
<a class="reference external" href="https://github.com/marl/crepe">https://github.com/marl/crepe</a></p>
<div class="section" id="automated-pitch-floor-and-ceiling-parameters">
<span id="floor-ceiling"></span><h3>Automated pitch floor and ceiling parameters<a class="headerlink" href="#automated-pitch-floor-and-ceiling-parameters" title="Permalink to this headline">¶</a></h3>
<p>Praat suggests adjusting pitch settings based on <a class="reference external" href="http://www.fon.hum.uva.nl/praat/manual/Intro_4_2__Configuring_the_pitch_contour.html">gender</a> . It’s not gender per se that is important, but the pitch of voice. To mitigate this, VoiceLab first casts a wide net in  floor and ceiling settings to learn the range of probable fundamental frequencies is a voice. Then it remeasures the voice pitch using different settings for higher and lower pitched voices. VoiceLab by default uses employs <code class="code python docutils literal notranslate"><span class="name"><span class="pre">very</span></span> <span class="name"><span class="pre">accurate</span></span></code>. VoiceLab returns <code class="code python docutils literal notranslate"><span class="name"><span class="pre">minimum</span></span> <span class="name"><span class="pre">pitch</span></span></code>, <code class="code python docutils literal notranslate"><span class="name"><span class="pre">maximum</span></span> <span class="name"><span class="pre">pitch</span></span></code>, <code class="code python docutils literal notranslate"><span class="name"><span class="pre">mean</span></span> <span class="name"><span class="pre">pitch</span></span></code>, and <code class="code python docutils literal notranslate"><span class="name"><span class="pre">standard</span></span> <span class="name"><span class="pre">deviation</span></span> <span class="name"><span class="pre">of</span></span> <span class="name"><span class="pre">pitch</span></span></code>. By default VoiceLab uses  autocorrelation for <a class="reference internal" href="#pitch"><span class="std std-ref">Measuring Pitch</span></a>, and cross-correlation for <a class="reference internal" href="#hnr"><span class="std std-ref">harmonicity</span></a>, <a class="reference internal" href="#jitter"><span class="std std-ref">Jitter</span></a>, and <a class="reference internal" href="#shimmer"><span class="std std-ref">Shimmer</span></a>,</p>
</div>
</div>
<div class="section" id="measure-harmonicity">
<span id="hnr"></span><h2>Measure Harmonicity<a class="headerlink" href="#measure-harmonicity" title="Permalink to this headline">¶</a></h2>
<p>This measures mean harmonics-to-noise-ratio using automatic floor and ceiling settings described <a class="reference internal" href="#floor-ceiling"><span class="std std-ref">here.</span></a>  Full details of the algorithm can be found in the <a href="#id1"><span class="problematic" id="id2">`Praat Manual Harmonicity Page&lt;http://www.fon.hum.uva.nl/praat/manual/Harmonicity.html&gt;`_</span></a>. By default Voice Lab use <code class="code python docutils literal notranslate"><span class="name"><span class="pre">To</span></span> <span class="name"><span class="pre">Harmonicity</span></span> <span class="punctuation"><span class="pre">(</span></span><span class="name"><span class="pre">cc</span></span><span class="punctuation"><span class="pre">)</span></span><span class="operator"><span class="pre">..</span></span></code>. You can select <code class="code python docutils literal notranslate"><span class="name"><span class="pre">To</span></span> <span class="name"><span class="pre">Harmonicity</span></span> <span class="punctuation"><span class="pre">(</span></span><span class="name"><span class="pre">ac</span></span><span class="punctuation"><span class="pre">)</span></span></code> or change any other Praat parameters if you wish.</p>
</div>
<div class="section" id="signal-to-noise-ratio">
<h2>Signal to Noise Ratio<a class="headerlink" href="#signal-to-noise-ratio" title="Permalink to this headline">¶</a></h2>
<p>This is the ratio between the loudest peak in the signal and the background noise. It is not Harmonics-to-Noise Ratio.</p>
</div>
<div class="section" id="measure-jitter">
<span id="jitter"></span><h2>Measure Jitter<a class="headerlink" href="#measure-jitter" title="Permalink to this headline">¶</a></h2>
<p>This measures and returns values of all of <a class="reference external" href="http://www.fon.hum.uva.nl/praat/manual/Voice_2__Jitter.html">Praat’s jitter algorithms</a>. This can be a bit overwhelming or difficult to understand which measure to use and why, or can lead to multiple colinear comparisons. To address this, by default, Voice Lab returns a the first component from a principal components analysis of those jitter algorithms taken across all selected voices. The underlying reasoning here is that each of these algorithms measures something about how noisy the voice is due to perturbations in period length. The PCA finds what is common about all of these measures of noise, and gives you a score relative to your sample. With a large enough sample, the PCA score should be a more robust measure of jitter than any single measurement. Voice Lab uses use it’s <a class="reference internal" href="#floor-ceiling"><span class="std std-ref">automated pitch floor and ceiling algorithm.</span></a> to set analysis parameters.</p>
<p>Jitter Measures:</p>
<ul class="simple">
<li><p>Jitter (local)</p></li>
<li><p>Jitter (local, absolute)</p></li>
<li><p>Jitter (rap)</p></li>
<li><p>Jitter (ppq5)</p></li>
<li><p>Jitter (ddp)</p></li>
</ul>
</div>
<div class="section" id="measure-shimmer">
<span id="shimmer"></span><h2>Measure Shimmer<a class="headerlink" href="#measure-shimmer" title="Permalink to this headline">¶</a></h2>
<p>This measures and returns values of all of <a class="reference external" href="http://www.fon.hum.uva.nl/praat/manual/Voice_3__Shimmer.html">Praat’s shimmer algorithms</a>. This can be a bit overwhelming or difficult to understand which measure to use and why, or can lead to multiple colinear comparisons. To address this, by default, Voice Lab returns a the first component from a principal components analysis of those shimmer algorithms taken across all selected voices. The underlying reasoning here is that each of these algorithms measures something about how noisy the voice is due to perturbations in amplitude of periods. The PCA finds what is common about all of these measures of noise, and gives you a score relative to your sample. With a large enough sample, the PCA score should be a more robust measure of shimmer than any single measurement. Voice Lab uses use it’s <a class="reference internal" href="#floor-ceiling"><span class="std std-ref">automated pitch floor and ceiling algorithm.</span></a> to set analysis parameters.</p>
<p>Shimmer Measures:</p>
<ul class="simple">
<li><p>Shimmer (local)</p></li>
<li><p>Shimmer (local, dB)</p></li>
<li><p>Shimmer (apq3)</p></li>
<li><p>Shimmer (aqp5)</p></li>
<li><p>Shimmer (apq11)</p></li>
<li><p>Shimmer (ddp)</p></li>
</ul>
</div>
<div class="section" id="measure-cepstral-peak-prominance-cpp">
<h2>Measure Cepstral Peak Prominance (CPP)<a class="headerlink" href="#measure-cepstral-peak-prominance-cpp" title="Permalink to this headline">¶</a></h2>
<p>This measures Cepstral Peak Prominance in Praat.
You can adjust interpolation, qeufrency upper and lower bounds, line type, and fit method.</p>
</div>
<div class="section" id="measure-formants">
<h2>Measure Formants<a class="headerlink" href="#measure-formants" title="Permalink to this headline">¶</a></h2>
<p>This returns the mean of the first 4 formant frequencies of the voice using the <code class="code python docutils literal notranslate"><span class="name"><span class="pre">To</span></span> <span class="name"><span class="pre">Burg</span></span></code> algorithm. We first <a class="reference internal" href="#pitch"><span class="std std-ref">measure pitch</span></a> to determine whether we should use 5500 Hz or 5000 Hz as the maximum formant value. <a class="reference external" href="http://www.fon.hum.uva.nl/praat/manual/Sound__To_Formant__burg____.html">Praat</a> suggests using 5500Hz for women, and 5000Hz for men, based on findings from Escudero, P., Boersma, P., Rauber, A. S., &amp; Bion, R. A. (2009). A cross-dialect acoustic description of vowels: Brazilian and European Portuguese. The Journal of the Acoustical Society of America, 126(3), 1379-1393. It is not gender per se that is the reason these settings are used, but that low fundamental frequency can confuse some measures of low-back vowels, and changing this setting helps fix that. Voice Lab will never ask you to assume the gender of a person, so it uses a cutoff of a 170 Hz Fundamental Frequency determine analysis parameters, rather than gender. Voice Lab always measure with a maximum of 5 formants in order to reliably measure 4 formants.</p>
</div>
<div class="section" id="vocal-tract-estimates">
<h2>Vocal Tract Estimates<a class="headerlink" href="#vocal-tract-estimates" title="Permalink to this headline">¶</a></h2>
<p>This returns the following vocal tract length estimates:</p>
<div class="section" id="average-formant">
<h3>Average Formant<a class="headerlink" href="#average-formant" title="Permalink to this headline">¶</a></h3>
<p>This calculates the mean <span class="math notranslate nohighlight">\(\frac {\sum _{i=1}^{n} {f_i}}{n}\)</span> of the first four formant frequencies for each sound.</p>
<p>Pisanski, K., &amp; Rendall, D. (2011). The prioritization of voice fundamental frequency or formants in listeners’ assessments of speaker size, masculinity, and attractiveness. The Journal of the Acoustical Society of America, 129(4), 2201-2212.</p>
</div>
<div class="section" id="principle-components-analysis">
<h3>Principle Components Analysis<a class="headerlink" href="#principle-components-analysis" title="Permalink to this headline">¶</a></h3>
<p>This returns the first factor from a Principle Components Analysis (PCA) of the 4 formants.</p>
<p>Babel, M., McGuire, G., &amp; King, J. (2014). Towards a more nuanced view of vocal attractiveness. PloS one, 9(2), e88616.</p>
</div>
<div class="section" id="formant-position">
<h3>Formant Position<a class="headerlink" href="#formant-position" title="Permalink to this headline">¶</a></h3>
<p>Formant Position is set to only run on samples of 30 or greater because this measure is based on transforming the data using z-scoring, which is based on the population mean. Without a large enough sample, this measurement could be suspicious.</p>
<p><span class="math notranslate nohighlight">\(\frac {\sum _{i=1}^{n} {f_i}}{n}\)</span></p>
<p>Puts, D. A., Apicella, C. L., &amp; Cárdenas, R. A. (2011). Masculine voices signal men’s threat potential in forager and industrial societies. Proceedings of the Royal Society B: Biological Sciences, 279(1728), 601-609.</p>
</div>
<div class="section" id="geometric-mean">
<h3>Geometric Mean<a class="headerlink" href="#geometric-mean" title="Permalink to this headline">¶</a></h3>
<p>This calculates the geometric mean <span class="math notranslate nohighlight">\(\left(\prod _{i=1}^{n}f_{i}\right)^{\frac {1}{n}}\)</span> of the first 4 formant frequencies for each sound.</p>
<p>Smith, D. R., &amp; Patterson, R. D. (2005). The interaction of glottal-pulse rate andvocal-tract length in judgements of speaker size, sex, and age.Journal of theAcoustical Society of America, 118, 3177e3186.</p>
</div>
<div class="section" id="formant-dispersion">
<h3>Formant Dispersion<a class="headerlink" href="#formant-dispersion" title="Permalink to this headline">¶</a></h3>
<p><span class="math notranslate nohighlight">\(\frac {\sum _{i=2}^{n} {f_i - f_{i-1}}}{n}\)</span></p>
<p>Fitch, W. T. (1997). Vocal-tract length and formant frequency dispersion correlate with body size in rhesus macaques.Journal of the Acoustical Society of America,102,1213e1222.</p>
</div>
<div class="section" id="vtl">
<h3>VTL<a class="headerlink" href="#vtl" title="Permalink to this headline">¶</a></h3>
<p><span class="math notranslate nohighlight">\(\frac {\sum _{i=1}^{n} (2n-1) \frac {f_i}{4c}}{n}\)</span></p>
<p>Fitch, W. T. (1997). Vocal-tract length and formant frequency dispersion correlate with body size in rhesus macaques.Journal of the Acoustical Society of America,102,1213e1222.</p>
<p>Titze, I. R. (1994).Principles of voice production. Englewood Cliffs, NJ: Prentice Hall.</p>
</div>
<div class="section" id="vtl-f">
<h3>VTL Δf<a class="headerlink" href="#vtl-f" title="Permalink to this headline">¶</a></h3>
<p><span class="math notranslate nohighlight">\(f_i\)</span> = The slope of 0 intercept regression between <span class="math notranslate nohighlight">\(F_i = \frac {(2i-1)}{2} Δf\)</span> and the mean of each of the first 4 formant frequencies.</p>
<p><span class="math notranslate nohighlight">\(VTL f_i = \frac {\sum _{i=1}^{n} (2n-1)(\frac {c}{4f_i})}{n}\)</span></p>
<p><span class="math notranslate nohighlight">\(VTL \Delta f = \frac {c}{2Δf}\)</span></p>
<p>Reby,D.,&amp;McComb,K.(2003).Anatomical constraints generate honesty: acoustic cues to age and weight in the roars of red deer stags. Animal Behaviour, 65,519e530.</p>
</div>
</div>
<div class="section" id="measure-intensity">
<h2>Measure Intensity<a class="headerlink" href="#measure-intensity" title="Permalink to this headline">¶</a></h2>
<p>This returns the mean of Praat’s  <code class="code python docutils literal notranslate"><span class="name"><span class="pre">Sound</span></span><span class="punctuation"><span class="pre">:</span></span> <span class="name"><span class="pre">To</span></span> <span class="name"><span class="pre">Intensity</span></span><span class="operator"><span class="pre">…</span></span></code> function in dB. You can adjust the minimum pitch parameter.</p>
</div>
<div class="section" id="measure-rms-energy">
<h2>Measure RMS Energy<a class="headerlink" href="#measure-rms-energy" title="Permalink to this headline">¶</a></h2>
<p>This returns RMS amplitude, similar to ENERGY in Voice Sauce. You can adjust the start and end time of the analysis. If you leave both values at 0, it will measure the entire file.</p>
</div>
<div class="section" id="measure-speech-rate">
<h2>Measure Speech Rate<a class="headerlink" href="#measure-speech-rate" title="Permalink to this headline">¶</a></h2>
<p>This function is an implementation of the Praat script published here:
De Jong, N.H. &amp; Wempe, T. (2009). Praat script to detect syllable nuclei and measure speech rate automatically. Behavior research methods, 41 (2), 385 - 390.</p>
<p>Voice Lab used version 2 of the script, available <cite>here https://sites.google.com/site/speechrate/Home/praat-script-syllable-nuclei-v2</cite></p>
<p>This returns:
- Number of Syllables</p>
<ul class="simple">
<li><p>Number of Pauses</p></li>
<li><p>Duratrion(s)</p></li>
<li><p>Phonation Time(s)</p></li>
<li><p>Speech Rate (Number of Syllables / Duration)</p></li>
<li><p>Articulation Rate (Number of Syllables / Phonation Time)</p></li>
<li><p>Average Syllable Duration (Speaking Time / Number of Syllables)</p></li>
</ul>
<p>You can adjust:
- silence threshold <code class="code python docutils literal notranslate"><span class="name"><span class="pre">mindb</span></span></code></p>
<ul class="simple">
<li><p>mimimum dip between peaks (dB) <code class="code python docutils literal notranslate"><span class="name"><span class="pre">mindip</span></span></code>. This should be between 2-4. Try 4 for clean and filtered sounds, and lower numbers for noisier sounds.</p></li>
<li><p>minimum pause length <code class="code python docutils literal notranslate"><span class="name"><span class="pre">minpause</span></span></code></p></li>
</ul>
<p>This command really only words on sounds with a few syllables, since Voice Lab is measuring how fast someone speaks. For monosyllabic sounds, use the <a class="reference internal" href="#duration"><span class="std std-ref">Measure Duration function.</span></a></p>
</div>
<div class="section" id="measure-spectral-tilt">
<h2>Measure Spectral Tilt<a class="headerlink" href="#measure-spectral-tilt" title="Permalink to this headline">¶</a></h2>
<p>This measures spectral tilt by returning the slope of a regression between freqeuncy and amplitude of each sound. This is from a script written by Michael J. Owren, with sorting errors corrected. This is not the same equation in Voice Sauce.</p>
<p>Owren, M.J. GSU Praat Tools: Scripts for modifying and analyzing sounds using Praat acoustics software. Behavior Research Methods (2008) 40:  822–829. <a class="reference external" href="https://doi.org/10.3758/BRM.40.3.822">https://doi.org/10.3758/BRM.40.3.822</a></p>
</div>
<div class="section" id="measure-ltas">
<h2>Measure LTAS:<a class="headerlink" href="#measure-ltas" title="Permalink to this headline">¶</a></h2>
<p>This measures several items from the Long-Term Average Spectrum using Praat’s default settings.</p>
<ul class="simple">
<li><p>mean (dB)</p></li>
<li><p>slope (dB)</p></li>
<li><p>local peak height (dB)</p></li>
<li><p>standard deviation (dB)</p></li>
<li><p>spectral tilt slope (dB/Hz)</p></li>
<li><p>spectral tilt intercept (dB)</p></li>
</ul>
<p>You can adjust:
- Pitch correction
- Bandwidth
- Max Frequency
- Shortest and longest periods
- Maximum period factor</p>
</div>
<div class="section" id="measure-spectral-shape">
<h2>Measure Spectral Shape<a class="headerlink" href="#measure-spectral-shape" title="Permalink to this headline">¶</a></h2>
<p>This measures spectral:
- Centre of Gravity
- Standard Deviation
- Kurtosis
- Band Energy Difference
- Band Density Difference</p>
</div>
<div class="section" id="teva">
<h2>TEVA<a class="headerlink" href="#teva" title="Permalink to this headline">¶</a></h2>
<p>This conducts TracheoEsophageal Voice Analysis, but can also be used on “normal” speech. Many of these measures are different than Praat functions, and overlap with other measures in Voice Lab.</p>
<p>This contains Praat scripts from: <a class="reference external" href="https://www.fon.hum.uva.nl/rob/NKI_TEVA/">https://www.fon.hum.uva.nl/rob/NKI_TEVA/</a>
This measures:
- Mean Pitch
- Standard Deviation of Pitch
- Median Pitch
- Voiced Fraction
- Harmonics to Noise Ratio
- Harmonics to Noise Ratio of high and low harmonics
- Jitter
- Shimmer
- Band Energy Difference
- Centre of gravity
- Maximum Voicing Duration
- Formant Quality
- AST (acoustic signal typing) score</p>
</div>
</div>
<div class="section" id="voice-manipulations">
<h1>Voice Manipulations<a class="headerlink" href="#voice-manipulations" title="Permalink to this headline">¶</a></h1>
<p>Voice Lab provides several voice manipulations.</p>
<div class="section" id="manipulate-pitch">
<h2>Manipulate Pitch<a class="headerlink" href="#manipulate-pitch" title="Permalink to this headline">¶</a></h2>
<p>This manipulates pitch using the PSOLA method. By default Manipulate Pitch Lower and Manipulate Pitch Higher lower and raise pitch by -/+ 0.5 ERBs (Equivalent Rectangular Bandwidths) which is about -/+ 20 Hz at a 120 Hz pitch centre and about -/+ 25 Hz at a 240 Hz pitch centre. By default VoiceLab also normalizes intensity to 70 dB RMS, but you can turn this off by deselecting the box in the Settings tab.</p>
</div>
<div class="section" id="manipulate-formants">
<h2>Manipulate Formants<a class="headerlink" href="#manipulate-formants" title="Permalink to this headline">¶</a></h2>
<p>This manipulates formants using Praat’s Change Gender Function. By default, Formants are scaled by +/- 15%. This manipulation resamples a sound by the Formant scaling factor (which can be altered in the Settings tab). Then, the sampling rate is overriden to the sound’s original sampling rate. Then PSOLA is employed to stretch time and pitch back (separately) into their original values.</p>
</div>
<div class="section" id="manipulate-pitch-and-formants">
<h2>Manipulate Pitch and Formants<a class="headerlink" href="#manipulate-pitch-and-formants" title="Permalink to this headline">¶</a></h2>
<p>This manipulation raises and lowers both pitch and formants in the same direction by the same or independent amounts. This uses the algorithm described in Manipulate Formants, but allows the user to scale or shift pitch to a designated degree. By default, pitch is also scaled +/- 15%.</p>
</div>
<div class="section" id="resample-sounds">
<h2>Resample Sounds<a class="headerlink" href="#resample-sounds" title="Permalink to this headline">¶</a></h2>
<p>This is a quick and easy way to batch process resampling sounds. 44.1kHz is the default. Change this value in the Settings tab.</p>
</div>
<div class="section" id="reverse-sounds">
<h2>Reverse Sounds<a class="headerlink" href="#reverse-sounds" title="Permalink to this headline">¶</a></h2>
<p>This reverses the selected sounds. Use this if you want to play sounds backwards. Try a Led Zepplin or Beatles song.</p>
</div>
<div class="section" id="scale-intensity">
<h2>Scale Intensity<a class="headerlink" href="#scale-intensity" title="Permalink to this headline">¶</a></h2>
<p>This scales intensity to an RMS value. Use this if you want your sounds to all be at an equivalent amplitude. By default intensity is normalized to 70 dB.</p>
</div>
</div>
<div class="section" id="spectrograms">
<h1>Spectrograms<a class="headerlink" href="#spectrograms" title="Permalink to this headline">¶</a></h1>
<p>VoiceLab creates full colour spectrograms. By default we use a wide-band window. You can adjust the window length. For example, for a narrow-band spectrogram, you can try 0.005 as a window length. You can also select a different colour palate. You can also overlay pitch, the first four formant frequencies, and intensity measures on the spectrogram.</p>
</div>
<div class="section" id="results-tab">
<h1>Results Tab<a class="headerlink" href="#results-tab" title="Permalink to this headline">¶</a></h1>
<p>This is where you can view results. You can select each voice file on the left and view each measurement result on the bottom frame. You can also view your spectrograms in the spectrogram window. You can change the size of any of these frames in order to see things better. Press <code class="code python docutils literal notranslate"><span class="name"><span class="pre">Save</span></span> <span class="name"><span class="pre">Results</span></span></code> to save data. All data (results &amp; settings), manipulated voices, and spectrograms are saved automatically when this button is pressed. All you have to do is choose which folder to save into. Don’t worry about picking file names, Voice Lab will make those automatically for you.</p>
<ul class="simple">
<li><p>All data files are saved as xlsx</p></li>
<li><p>All sound files are saved as wav</p></li>
<li><p>All image files are saved as png</p></li>
</ul>
</div>


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